Machine learning-Based Prediction of Home Prices

In the past few years, machine learning has played an effective part in product orders and recommendations in the medical industry and every day. In return, we offer great customer support and safer automotive systems. As a result of all that has been illustrated, ML may be a innovation that’s getting to be increasingly prevalent over a wide range of businesses. The genuine domain advertise is one of the foremost competitive and price-focused in presence nowadays. People are endeavouring to buy a modern domestic by considering their budgets and showcase procedure. However, the essential blemish within the existing framework is that it decides the cost of a house without making the fundamental expectations around future advertise patterns, which leads to an increment in cost. Our project’s essential objective is to precisely expect the cost of a house without causing any misfortunes. A few components must be taken into consideration in arrange to accurately anticipate domestic costs and to supply customers with compelling house estimating that’s in line with their inclinations and budget. So, we are creating a show to assess lodging costs. using machine learning advances such as K-Means, Irregular Woodland, Choice Tree, and Direct Relapse. This strategy allows consumers to finance real estate without going through a broker. Research results show that random forest regression has the highest accuracy.

I. Introduction

In the past few years, machine learning has grown into a powerful tool for predicting home prices. With the availability of vast amounts of data and powerful algorithms, machine learning models can learn complex patterns and relationships that exist within real estate data, making accurate predictions possible. House price prediction is a complex problem that involves analyzing a variety of factors, like area, measure, number of rooms and washrooms, and general condition of the property. [1] Traditional approaches to house price prediction have relied on simple statistical models that rely on linear relationships between these factors. [4] However, these models often fail to capture the complex, nonlinear relationships that exist between the various factors, resulting in inaccurate predictions. Machine learning, on the other hand, is able to capture these complex relationships by using sophisticated algorithms that can learn from data. By feeding large amounts of real estate data into a machine learning model, the model can identify patterns and relationships that traditional statistical models are unable to discern. This allows the model to make more accurate predictions, even in cases where the relationships between the various factors are not immediately obvious. [10] One of the most prominent machine learning algos for house price prediction is the regression algorithm. This algorithm works by identifying the relationship between a set of independent variables (such as location, size, bedrooms and bathrooms) and a dependent variable (the price of the property). [21] By analyzing this relationship, the algorithm is able to make predictions about the price of a property based on its characteristics. Another popular machine learning algorithm for predicting house prices is the decision tree algorithm. [12] The algorithm works by dividing complex problems into smaller, more manageable problems. By doing this, the algorithm can identify the most important factors affecting real estate prices and make predictions based on these factors. [9]